A Lightweight UWB NLOS Detection Algorithm Using Referenced CIR Similarity Metrics

A lightweight and effective algorithm is benefit for real-world applications and edge devices. This letter presents a lightweight algorithm for Non-Line-of-Sight (NLOS) detection in Ultra-Wideband (UWB) systems, using the referenced Channel Impulse Response (CIR) similarity metrics. It retains essential UWB channel characteristics while incorporating a Radar Cross-Section (RCS)-inspired feature engineering method to capture the effective NLOS representation. It bears novel features to significantly reduce the input data dimensions, model parameters, and Floating-point operations (Flops) by 64.9%, 78.5%, 76.0%, respectively, with comparable performance to the latest State-Of-The-Art (SOTA) solutions. We demonstrate its effectiveness through actual measurements and comparative experiments in mixed complex Line-Of-Sight (LOS)/NLOS dataset, offering a computationally efficient solution for UWB NLOS detection with effective representation.

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Publication Details

Journal
IEEE Wireless Communications Letters
Published
2024-08-20
DOI
https://doi.org/10.1109/lwc.2024.3446632
Citations
4
Primary Topic
Ultra-Wideband Communications Technology
Type
article
Field-Weighted Citation Impact
0.83

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article

A Lightweight UWB NLOS Detection Algorithm Using Referenced CIR Similarity Metrics

Fanxing Yang, Lingfei Mo, Youdong Zhang, Xu He et al.
4 citations
IEEE Wireless Communications Letters
Ultra-Wideband Communications Technology
0.83
article

A Lightweight UWB NLOS Detection Algorithm Using Referenced CIR Similarity Metrics

Fanxing Yang, Lingfei Mo, Youdong Zhang, Xu He, Qing Wang, Xiaolin Meng, Runkun Yang
article en
4 citations

Abstract

A lightweight and effective algorithm is benefit for real-world applications and edge devices. This letter presents a lightweight algorithm for Non-Line-of-Sight (NLOS) detection in Ultra-Wideband (UWB) systems, using the referenced Channel Impulse Response (CIR) similarity metrics. It retains essential UWB channel characteristics while incorporating a Radar Cross-Section (RCS)-inspired feature engineering method to capture the effective NLOS representation. It bears novel features to significantly reduce the input data dimensions, model parameters, and Floating-point operations (Flops) by 64.9%, 78.5%, 76.0%, respectively, with comparable performance to the latest State-Of-The-Art (SOTA) solutions. We demonstrate its effectiveness through actual measurements and comparative experiments in mixed complex Line-Of-Sight (LOS)/NLOS dataset, offering a computationally efficient solution for UWB NLOS detection with effective representation.

IEEE Wireless Communications LettersVol. 13(10)
Southeast University (CN)
National Key Research and Development Program of China, Innovation Capability Development Project of Jiangsu Province
Openalex Percentile: Top 27%
Ultra-Wideband Communications Technology
0.83
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